Mr. Yumeng Su | Engineering | Best Researcher Award

Mr. Yumeng Su at Shanghai Jian Qiao University, China.

Su Yumeng 🎓, a top-ranking computer science undergraduate at Shanghai Jian Qiao University 🇨🇳, excels in AI 🤖, robotics 🚁, and embedded systems 🧠. With hands-on experience in drone development, ROS, and deep learning 💡, he has published impactful research and led award-winning teams 🏆. His technical expertise spans Python, MATLAB, LaTeX, and advanced hardware platforms like Jetson Nano and STM32 💻. Beyond academics, he’s a dynamic leader and athlete 🏀🚴‍♂️, known for his resilience and innovation in intelligent systems and smart hardware applications 🔍. Su’s work bridges theoretical AI with real-world applications 🌐.

Publication Top Notes

ORCID

Suitability for Best Researcher Award – Su Yumeng

Su Yumeng is a highly promising and exceptionally driven early-career researcher whose blend of technical excellence, innovation, and leadership places him as a top contender for the Best Researcher Award. As an undergraduate, his hands-on contributions to AI, robotics, and embedded systems are not only commendable but groundbreaking, particularly for his academic level. He demonstrates a rare ability to translate theory into impactful real-world applications, bridging research with innovation in autonomous systems, drone technology, and intelligent hardware solutions.

🔹 Education & Experience

• 🎓 B.Sc. in Computer Science & Technology, Shanghai Jian Qiao University (2021–Present)

• 📍 Focus: AI, Robotics, Embedded Systems, and Smart Hardware

• 📚 Completed key courses with top grades (AI, Python, Robotics, Microcontroller Principles, etc.)

• 🚀 ROS training at East China Normal University (Basic & Advanced UAV/Vehicle Tracking)

• 🛠 Internship at Superdimension Technology Space: Autonomous drone development

• 🧪 Project collaboration with FAST-Lab at Zhejiang University on UAVs

🔹 Professional Development

Su Yumeng continually advances his professional skills through academic projects 🧪, interdisciplinary competitions 🏆, and real-world UAV applications 🚁. He has mastered the integration of AI models like YOLO with edge computing platforms such as Jetson Nano and Raspberry Pi 💻. His leadership in innovation competitions reflects his capacity to guide teams and deliver impactful solutions 🎯. Su’s deep involvement in research and drone design demonstrates his ability to convert academic concepts into cutting-edge technology 💡. With practical ROS experience and sensor fusion expertise, he remains at the forefront of smart automation and robotics 🌐.

🔹 Research Focus Category

Su Yumeng’s research focuses on Artificial Intelligence in Embedded and Autonomous Systems 🤖, especially in smart robotics and deep learning applications for environmental perception and control 🌍. His work bridges physics-informed neural networks (PINNs) with real-time sensor fusion for drones and robotics 🤝. He explores practical challenges like crack detection in infrastructure using UAVs 🛠️, baby posture recognition on embedded platforms 🍼, and SLAM-based navigation for wheeled robots 🚗. His interdisciplinary approach merges hardware innovation with AI, yielding scalable, intelligent, and responsive systems suitable for civil engineering, healthcare, and autonomous mobility fields 🚀.

🔹 Awards & Honors

• 🥇 National Second Prize, 17th National College Student Computer Design Competition (2024)

• 🥉 National Bronze & Shanghai Gold, China Innovation Competition (2024)

• 🥇 Shanghai Gold Award, Career Planning Competition (2024)

• 🏅 First Prize, Shanghai College Student Computer Application Competition (2024)

• 🥈 Shanghai Second Prize, Ti Cup Electronic Design Contest (2023)

• 🥉 Bronze Award, “Challenge Cup” Entrepreneurship Plan Competition

• 🥉 Shanghai Third Prize, China Robot & AI Competition (2024)

• 🎖 National Motivational Scholarship ×3

• 🎓 President “Qing Yun” Scholarship

• 🎗 School Special Scholarship

Publication Top Notes

• “The Feasibility Assessment Study of Bridge Crack Width Recognition in Images Based on Special Inspection UAV”
  Cited by: 13 | Year: 2020 ​

• “Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning-Assisted Image Processing Approach”
  Cited by: 20 | Year: 2022